QQ客服 官方微博 反馈留言

	Classification with the hybrid of manifold learning and gabor wavelet
	Zhang, Junping ; Shen, Chao ; Feng, Jufu
	2006
英文摘要	While manifold learning algorithms can discover intrinsic low-dimensional manifold embedded in the high-dimensional Euclidean space, the discriminant ability of the low-dimensional subspaces obtained by the algorithms is often lower than those obtained by the conventional dimensionality reduction approaches. Furthermore, the original feature vectors may include redundancy such as high-order correlation which cannot be removed by manifold learning algorithms. To address the two problems, we first employ Gabor wavelet to remove intrinsic redundancies of images and obtain a set of over-completed feature vectors. Then a supervised manifold learning algorithm (ULLELDA) is applied to project Gabor-based data and out-of-the-samples into a common low-dimensional subspace. Experiments in two FERET face databases indicate that Gabors indeed help supervised manifold learning to remarkably improve the discriminant ability of low-dimensional subspaces. ? Springer-Verlag Berlin Heidelberg 2006.; EI; 0
语种	英语
DOI标识	10.1007/11759966_200
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/328112]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Zhang, Junping,Shen, Chao,Feng, Jufu. Classification with the hybrid of manifold learning and gabor wavelet. 2006-01-01.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。